Instructor Notes
Not done yet 😬️. Sorry! Here’s a copy of the Discussion Questions instead.
- What defines a state space model?
- What is the difference between process and observation error?
- What are the advantages of modeling them separately?
- What is the benefit of making predictions of the underlying state even if we can’t observe it?
- What are some of the complexities that can be incorporated into state space models?
- How is the normal dynamic linear model constructed?
- How is nonlinearity integrated?
- Why is fitting state space models challenging?
- How might these models be useful for modeling time-series and making forecasts in ecology